GenAI Engineer

Job description / Role

Description

DeepLight AI is a specialist consultancy implementing intelligent enterprise systems, with deep expertise in financial services and banking. We are seeking a Generative AI Engineer to join our team, focusing on turning advanced research into high-impact, production-ready enterprise solutions.

You won't just train models; you will design, build, and own the entire lifecycle of AI systems that leverage large language models (LLMs), retrieval-augmented generation (RAG), and multimodal AI to solve our clients' most complex problems.

The challenge: owning the LLM production pipeline

This role demands a unique combination of applied machine learning, data engineering, and scalable software delivery, operating entirely within a robust MLOps/LLMOps framework.

What you will be doing (key responsibilities):

• Design & deliver GenAI solutions: Leading the implementation of LLM-based applications, including custom chatbots, advanced summarization services, and creative co-pilots.
• Model optimization & steering: Applying cutting-edge techniques like fine-tuning, LoRA/PEFT, and RLHF to optimize model performance and ensure factual accuracy.
• RAG system architecture: Architecting and building high-performance retrieval-augmented generation pipelines, managing the full lifecycle of embeddings and context-aware retrieval.
• Production code: Writing robust, deployment-ready software in Python and TypeScript, delivering clean, modular code.
• MLOps automation: Implementing full LLMOps/MLOps using Docker, Kubernetes, and Terraform to automate CI/CD, deployment, and versioning across major cloud platforms (AWS/GCP/Azure).
• Data grounding: Ensuring model intelligence is fresh and reliable by connecting to Airflow, dbt, and Kafka pipelines.
• Governance & safety: Embedding responsible AI practices, including hallucination control, bias mitigation, and auditability, into every deployed system.

Requirements

• Deep knowledge of transformers, diffusion models, and advanced prompt engineering.
• Proven expertise in embeddings, vector databases, and designing context-aware retrieval pipelines.
• Proficiency in Python, REST APIs, containerization, CI/CD, and model versioning.
• Proficiency in Python and TypeScript; core ML development using PyTorch, TensorFlow, and the Hugging Face ecosystem.
• Expertise with LangChain, LlamaIndex, and APIs from OpenAI/Anthropic.
• Use of MLflow and Weights & Biases for experiment tracking and versioning, leveraging platforms like Vertex AI or SageMaker.
• Mastery of Docker, Kubernetes, and Terraform for scalable deployment across major clouds (AWS, GCP, Azure).
• Familiarity with Airflow, dbt, Postgres, and Kafka for data ingestion and transformation.

Experience & qualifications

• 3–7 years in AI/ML, with a critical 1–3 years hands-on with LLMs or production GenAI applications.
• Proven delivery of production-grade GenAI tools in agile, rapid-iteration environments.
• BSc/MSc in Computer Science, Data Science, or a related technical field.
• Experience with model evaluation, data privacy, and bias mitigation.
• Hands-on experience with ChromaDB, Pinecone, Weaviate, and FAISS for retrieval and embedding management.

Benefits

• Competitive salary
• Comprehensive health insurance
• Ongoing professional development
• Opportunity to work on cutting-edge AI projects
• Flexible working arrangements
• Career advancement opportunities in a rapidly growing AI company

This position offers a unique opportunity to shape the future of AI implementation while working with a talented team of professionals at the forefront of technological innovation. The successful candidate will play a crucial role in driving our company's success in delivering transformative AI solutions to our clients.

Please note: If you fail to meet the required criteria in the screening questions, your application will not be progressed.